Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank
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  • 英文篇名:Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank
  • 作者:Jalil ; Pazhoohan ; Hossein ; Beiki ; Morteza ; EsfANDyari
  • 英文作者:Jalil Pazhoohan;Hossein Beiki;Morteza EsfANDyari;Department of Chemical Engineering, Quchan University of Technology;Department of chemical engineering, University of Bojnord;
  • 英文关键词:flotation tailings;;leaching;;copper;;environments;;adaptive neural fuzzy inference system
  • 中文刊名:BJKY
  • 英文刊名:矿物冶金与材料学报(英文版)
  • 机构:Department of Chemical Engineering, Quchan University of Technology;Department of chemical engineering, University of Bojnord;
  • 出版日期:2019-05-10
  • 出版单位:International Journal of Minerals Metallurgy and Materials
  • 年:2019
  • 期:v.26;No.175
  • 语种:英文;
  • 页:BJKY201905002
  • 页数:9
  • CN:05
  • ISSN:11-5787/TF
  • 分类号:10-18
摘要
The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40 wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system(ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.
        The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40 wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system(ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.
引文
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